Commit graph

18 commits

Author SHA1 Message Date
Aliaksandr Valialkin
b7cc1af3eb
app/vmselect/promql/aggr_incremental.go: eliminate unnecessary memory allocation in incrementalAggrFuncContext.updateTimeseries 2024-01-23 02:29:13 +02:00
Aliaksandr Valialkin
9661918bb4
app/vmselect/promql: optimize repeated SLI-like instant queries with lookbehind windows >= 1d
Repeated instant queries with long lookbehind windows, which contain one of the following rollup functions,
are optimized via partial result caching:

- sum_over_time()
- count_over_time()
- avg_over_time()
- increase()
- rate()

The basic idea of optimization is to calculate

  rf(m[d] @ t)

as

  rf(m[offset] @ t) + rf(m[d] @ (t-offset)) - rf(m[offset] @ (t-d))

where rf(m[d] @ (t-offset)) is cached query result, which was calculated previously

The offset may be in the range of up to 1 hour.
2023-10-31 20:08:38 +01:00
Nikolay
4a50e9400c
app/vmselect: reduce lock contention for heavy aggregation requests (#5119)
reduce lock contention for heavy aggregation requests
previously lock contetion may happen on machine with big number of CPU due to enabled string interning. sync.Map was a choke point for all aggregation requests.
Now instead of interning, new string is created. It may increase CPU and memory usage for some cases.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5087
2023-10-10 13:44:02 +02:00
Aliaksandr Valialkin
18af01c387
app/vmselect: optimize incremental aggregates a bit
Substitute sync.Map with an ordinary slice indexed by workerID.
This should reduce the overhead when updating the incremental aggregate state
2023-03-20 15:42:13 -07:00
Aliaksandr Valialkin
c0de651558
app/vmselect/promql: intern output series names during incremental aggregation
This should reduce the number of memory allocations for repeated queries
2023-01-09 22:12:05 -08:00
Aliaksandr Valialkin
5497997b72
app/vmselect/promql: increase scalability of incremental aggregate calculations on systems with many CPU cores
Use sync.Map instead of a global mutex there. This should lift scalability limits
on systems with many CPU cores.
2022-10-01 20:14:14 +03:00
Aliaksandr Valialkin
9dccedc599 app/vmselect/promql: return empty values from group() if all the time series have no values at the given timestamp
This aligns `group()` behaviour to Prometheus
2020-07-28 13:41:04 +03:00
Aliaksandr Valialkin
eb402a17bd app/vmselect/promql: optimize group(rollup(m)) calculations 2020-07-17 16:47:30 +03:00
Aliaksandr Valialkin
f3d9a5b0ec app/vmselect/promql: suppress "SA4006: this value of dstValues is never used" error in golangci-lint 2020-05-13 11:46:05 +03:00
Aliaksandr Valialkin
18a0caee43 app/vmselect/promql: fix any(..) calculations - return all the data points instead of the first one 2020-05-12 20:36:49 +03:00
Aliaksandr Valialkin
408ade27a9 app/vmselect/promql: add any(x) by (y) aggregate function, which returns any time series from q for each group y 2020-05-12 19:50:29 +03:00
Aliaksandr Valialkin
21c2982ac8 app/vmselect/promql: support for sum(x) by (y) limit N syntax in order to limit the number of output time series after aggregation 2020-05-12 19:50:12 +03:00
Aliaksandr Valialkin
9ed4951ec8 lib/metricsql: move it to a separate repository - github.com/VictoriaMetrics/metrics 2020-04-28 15:30:06 +03:00
Aliaksandr Valialkin
453d71d082 Rename lib/promql to lib/metricsql and apply small fixes 2019-12-25 22:09:09 +02:00
Mike Poindexter
009d1559db Split Extended PromQL parsing to a separate library 2019-12-25 22:09:07 +02:00
Aliaksandr Valialkin
aac482517f app/vmselect/promql: return NaN from count() over zero time series
This aligns `count` behavior with Prometheus.
2019-07-25 22:02:34 +03:00
Aliaksandr Valialkin
6875fb411a app/vmselect/promql: parallelize incremental aggregation to multiple CPU cores
This may reduce response times for aggregation over big number of time series
with small step between output data points.
2019-07-12 15:53:12 +03:00
Aliaksandr Valialkin
cbab86fd9d app/vmselect/promql: reduce RAM usage for aggregates over big number of time series
Calculate incremental aggregates for `aggr(metric_selector)` function instead of
keeping all the time series matching the given `metric_selector` in memory.
2019-07-10 13:03:36 +03:00